Overview

Brought to you by YData

Dataset statistics

Number of variables15
Number of observations713
Missing cells1
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory523.5 KiB
Average record size in memory751.9 B

Variable types

Text8
Numeric6
Categorical1

Alerts

Gross is highly overall correlated with No_of_VotesHigh correlation
No_of_Votes is highly overall correlated with IMDB_Rating and 1 other fieldsHigh correlation
IMDB_Rating is highly overall correlated with No_of_VotesHigh correlation
Released_Year is highly overall correlated with CertificateHigh correlation
Certificate is highly overall correlated with Released_YearHigh correlation
Series_Title has unique values Unique
Overview has unique values Unique
No_of_Votes has unique values Unique

Reproduction

Analysis started2025-08-29 01:08:32.569386
Analysis finished2025-08-29 01:08:36.388885
Duration3.82 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

Series_Title
Text

Unique 

Distinct713
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size46.0 KiB
2025-08-28T22:08:36.542885image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Length

Max length68
Median length41
Mean length15.652174
Min length2

Characters and Unicode

Total characters11160
Distinct characters90
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique713 ?
Unique (%)100.0%

Sample

1st rowThe Godfather
2nd rowThe Dark Knight
3rd rowThe Godfather: Part II
4th row12 Angry Men
5th rowThe Lord of the Rings: The Return of the King
ValueCountFrequency (%)
the 219
 
10.9%
of 63
 
3.1%
a 21
 
1.0%
and 21
 
1.0%
in 18
 
0.9%
no 15
 
0.7%
la 15
 
0.7%
2 11
 
0.5%
de 11
 
0.5%
to 11
 
0.5%
Other values (1212) 1612
79.9%
2025-08-28T22:08:36.933108image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1304
 
11.7%
e 1055
 
9.5%
a 757
 
6.8%
o 711
 
6.4%
n 661
 
5.9%
r 620
 
5.6%
i 615
 
5.5%
t 575
 
5.2%
h 408
 
3.7%
s 405
 
3.6%
Other values (80) 4049
36.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 11160
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1304
 
11.7%
e 1055
 
9.5%
a 757
 
6.8%
o 711
 
6.4%
n 661
 
5.9%
r 620
 
5.6%
i 615
 
5.5%
t 575
 
5.2%
h 408
 
3.7%
s 405
 
3.6%
Other values (80) 4049
36.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 11160
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1304
 
11.7%
e 1055
 
9.5%
a 757
 
6.8%
o 711
 
6.4%
n 661
 
5.9%
r 620
 
5.6%
i 615
 
5.5%
t 575
 
5.2%
h 408
 
3.7%
s 405
 
3.6%
Other values (80) 4049
36.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 11160
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1304
 
11.7%
e 1055
 
9.5%
a 757
 
6.8%
o 711
 
6.4%
n 661
 
5.9%
r 620
 
5.6%
i 615
 
5.5%
t 575
 
5.2%
h 408
 
3.7%
s 405
 
3.6%
Other values (80) 4049
36.3%

Released_Year
Real number (ℝ)

High correlation 

Distinct82
Distinct (%)11.5%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean1995.7388
Minimum1930
Maximum2019
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.7 KiB
2025-08-28T22:08:37.027790image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum1930
5-th percentile1958.55
Q11986.75
median2001
Q32010
95-th percentile2017
Maximum2019
Range89
Interquartile range (IQR)23.25

Descriptive statistics

Standard deviation18.611182
Coefficient of variation (CV)0.0093254601
Kurtosis0.92688061
Mean1995.7388
Median Absolute Deviation (MAD)11
Skewness-1.1460661
Sum1420966
Variance346.3761
MonotonicityNot monotonic
2025-08-28T22:08:37.114948image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2014 28
 
3.9%
2004 27
 
3.8%
2001 24
 
3.4%
2009 23
 
3.2%
2013 22
 
3.1%
2007 21
 
2.9%
2006 21
 
2.9%
2016 20
 
2.8%
2003 19
 
2.7%
1993 19
 
2.7%
Other values (72) 488
68.4%
ValueCountFrequency (%)
1930 1
 
0.1%
1931 1
 
0.1%
1933 1
 
0.1%
1934 1
 
0.1%
1936 1
 
0.1%
1938 1
 
0.1%
1939 3
0.4%
1940 3
0.4%
1941 1
 
0.1%
1942 1
 
0.1%
ValueCountFrequency (%)
2019 15
2.1%
2018 12
1.7%
2017 18
2.5%
2016 20
2.8%
2015 18
2.5%
2014 28
3.9%
2013 22
3.1%
2012 14
2.0%
2011 14
2.0%
2010 18
2.5%

Certificate
Categorical

High correlation 

Distinct9
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size35.4 KiB
U
183 
A
173 
UA
143 
R
131 
PG-13
38 
Other values (4)
45 

Length

Max length8
Median length1
Mean length1.5946704
Min length1

Characters and Unicode

Total characters1137
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowA
2nd rowUA
3rd rowA
4th rowU
5th rowU

Common Values

ValueCountFrequency (%)
U 183
25.7%
A 173
24.3%
UA 143
20.1%
R 131
18.4%
PG-13 38
 
5.3%
PG 20
 
2.8%
Approved 15
 
2.1%
G 9
 
1.3%
Other 1
 
0.1%

Length

2025-08-28T22:08:37.199413image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-28T22:08:37.288940image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
u 183
25.7%
a 173
24.3%
ua 143
20.1%
r 131
18.4%
pg-13 38
 
5.3%
pg 20
 
2.8%
approved 15
 
2.1%
g 9
 
1.3%
other 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
A 331
29.1%
U 326
28.7%
R 131
 
11.5%
G 67
 
5.9%
P 58
 
5.1%
- 38
 
3.3%
1 38
 
3.3%
3 38
 
3.3%
p 30
 
2.6%
r 16
 
1.4%
Other values (7) 64
 
5.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1137
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 331
29.1%
U 326
28.7%
R 131
 
11.5%
G 67
 
5.9%
P 58
 
5.1%
- 38
 
3.3%
1 38
 
3.3%
3 38
 
3.3%
p 30
 
2.6%
r 16
 
1.4%
Other values (7) 64
 
5.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1137
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 331
29.1%
U 326
28.7%
R 131
 
11.5%
G 67
 
5.9%
P 58
 
5.1%
- 38
 
3.3%
1 38
 
3.3%
3 38
 
3.3%
p 30
 
2.6%
r 16
 
1.4%
Other values (7) 64
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1137
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 331
29.1%
U 326
28.7%
R 131
 
11.5%
G 67
 
5.9%
P 58
 
5.1%
- 38
 
3.3%
1 38
 
3.3%
3 38
 
3.3%
p 30
 
2.6%
r 16
 
1.4%
Other values (7) 64
 
5.6%

Runtime
Real number (ℝ)

Distinct118
Distinct (%)16.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean123.69004
Minimum72
Maximum238
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.7 KiB
2025-08-28T22:08:37.379755image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum72
5-th percentile90
Q1104
median120
Q3136
95-th percentile174.4
Maximum238
Range166
Interquartile range (IQR)32

Descriptive statistics

Standard deviation25.896632
Coefficient of variation (CV)0.20936715
Kurtosis1.3731226
Mean123.69004
Median Absolute Deviation (MAD)16
Skewness1.0116596
Sum88191
Variance670.63553
MonotonicityNot monotonic
2025-08-28T22:08:37.466394image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
130 20
 
2.8%
101 18
 
2.5%
100 17
 
2.4%
129 17
 
2.4%
102 17
 
2.4%
122 16
 
2.2%
127 15
 
2.1%
120 15
 
2.1%
113 15
 
2.1%
108 14
 
2.0%
Other values (108) 549
77.0%
ValueCountFrequency (%)
72 1
 
0.1%
76 1
 
0.1%
78 1
 
0.1%
80 3
0.4%
81 3
0.4%
82 2
0.3%
83 2
0.3%
84 2
0.3%
85 3
0.4%
86 4
0.6%
ValueCountFrequency (%)
238 1
0.1%
228 1
0.1%
224 1
0.1%
212 1
0.1%
209 1
0.1%
207 1
0.1%
202 2
0.3%
201 1
0.1%
197 2
0.3%
195 1
0.1%

Genre
Text

Distinct172
Distinct (%)24.1%
Missing0
Missing (%)0.0%
Memory size47.8 KiB
2025-08-28T22:08:37.557732image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Length

Max length29
Median length24
Mean length19.453015
Min length5

Characters and Unicode

Total characters13870
Distinct characters33
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique64 ?
Unique (%)9.0%

Sample

1st rowCrime, Drama
2nd rowAction, Crime, Drama
3rd rowCrime, Drama
4th rowCrime, Drama
5th rowAction, Adventure, Drama
ValueCountFrequency (%)
drama 499
27.3%
adventure 163
 
8.9%
comedy 161
 
8.8%
crime 142
 
7.8%
action 140
 
7.7%
thriller 99
 
5.4%
biography 88
 
4.8%
romance 88
 
4.8%
mystery 70
 
3.8%
animation 63
 
3.4%
Other values (11) 316
17.3%
2025-08-28T22:08:37.728966image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 1430
 
10.3%
r 1320
 
9.5%
, 1116
 
8.0%
1116
 
8.0%
m 1002
 
7.2%
e 918
 
6.6%
i 838
 
6.0%
o 637
 
4.6%
n 588
 
4.2%
t 562
 
4.1%
Other values (23) 4343
31.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 13870
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 1430
 
10.3%
r 1320
 
9.5%
, 1116
 
8.0%
1116
 
8.0%
m 1002
 
7.2%
e 918
 
6.6%
i 838
 
6.0%
o 637
 
4.6%
n 588
 
4.2%
t 562
 
4.1%
Other values (23) 4343
31.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 13870
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 1430
 
10.3%
r 1320
 
9.5%
, 1116
 
8.0%
1116
 
8.0%
m 1002
 
7.2%
e 918
 
6.6%
i 838
 
6.0%
o 637
 
4.6%
n 588
 
4.2%
t 562
 
4.1%
Other values (23) 4343
31.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 13870
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 1430
 
10.3%
r 1320
 
9.5%
, 1116
 
8.0%
1116
 
8.0%
m 1002
 
7.2%
e 918
 
6.6%
i 838
 
6.0%
o 637
 
4.6%
n 588
 
4.2%
t 562
 
4.1%
Other values (23) 4343
31.3%

IMDB_Rating
Real number (ℝ)

High correlation 

Distinct16
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.9352034
Minimum7.6
Maximum9.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.7 KiB
2025-08-28T22:08:37.797549image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum7.6
5-th percentile7.6
Q17.7
median7.9
Q38.1
95-th percentile8.5
Maximum9.2
Range1.6
Interquartile range (IQR)0.4

Descriptive statistics

Standard deviation0.28899865
Coefficient of variation (CV)0.036419816
Kurtosis1.269796
Mean7.9352034
Median Absolute Deviation (MAD)0.2
Skewness1.1171782
Sum5657.8
Variance0.083520218
MonotonicityDecreasing
2025-08-28T22:08:37.871037image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
7.7 121
17.0%
7.8 107
15.0%
7.6 107
15.0%
8 97
13.6%
7.9 77
10.8%
8.1 72
10.1%
8.3 33
 
4.6%
8.2 32
 
4.5%
8.4 20
 
2.8%
8.5 19
 
2.7%
Other values (6) 28
 
3.9%
ValueCountFrequency (%)
7.6 107
15.0%
7.7 121
17.0%
7.8 107
15.0%
7.9 77
10.8%
8 97
13.6%
8.1 72
10.1%
8.2 32
 
4.5%
8.3 33
 
4.6%
8.4 20
 
2.8%
8.5 19
 
2.7%
ValueCountFrequency (%)
9.2 1
 
0.1%
9 3
 
0.4%
8.9 3
 
0.4%
8.8 5
 
0.7%
8.7 5
 
0.7%
8.6 11
 
1.5%
8.5 19
2.7%
8.4 20
2.8%
8.3 33
4.6%
8.2 32
4.5%

Overview
Text

Unique 

Distinct713
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size139.9 KiB
2025-08-28T22:08:38.049876image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Length

Max length313
Median length196
Mean length148.75736
Min length40

Characters and Unicode

Total characters106064
Distinct characters81
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique713 ?
Unique (%)100.0%

Sample

1st rowAn organized crime dynasty's aging patriarch transfers control of his clandestine empire to his reluctant son.
2nd rowWhen the menace known as the Joker wreaks havoc and chaos on the people of Gotham, Batman must accept one of the greatest psychological and physical tests of his ability to fight injustice.
3rd rowThe early life and career of Vito Corleone in 1920s New York City is portrayed, while his son, Michael, expands and tightens his grip on the family crime syndicate.
4th rowA jury holdout attempts to prevent a miscarriage of justice by forcing his colleagues to reconsider the evidence.
5th rowGandalf and Aragorn lead the World of Men against Sauron's army to draw his gaze from Frodo and Sam as they approach Mount Doom with the One Ring.
ValueCountFrequency (%)
a 1144
 
6.3%
the 904
 
5.0%
to 597
 
3.3%
of 555
 
3.1%
and 505
 
2.8%
in 406
 
2.2%
his 379
 
2.1%
an 205
 
1.1%
with 180
 
1.0%
is 169
 
0.9%
Other values (4801) 13075
72.2%
2025-08-28T22:08:38.330154image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
17406
16.4%
e 10019
 
9.4%
a 7094
 
6.7%
t 6765
 
6.4%
i 6376
 
6.0%
o 6290
 
5.9%
n 6151
 
5.8%
r 5982
 
5.6%
s 5751
 
5.4%
h 4066
 
3.8%
Other values (71) 30164
28.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 106064
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
17406
16.4%
e 10019
 
9.4%
a 7094
 
6.7%
t 6765
 
6.4%
i 6376
 
6.0%
o 6290
 
5.9%
n 6151
 
5.8%
r 5982
 
5.6%
s 5751
 
5.4%
h 4066
 
3.8%
Other values (71) 30164
28.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 106064
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
17406
16.4%
e 10019
 
9.4%
a 7094
 
6.7%
t 6765
 
6.4%
i 6376
 
6.0%
o 6290
 
5.9%
n 6151
 
5.8%
r 5982
 
5.6%
s 5751
 
5.4%
h 4066
 
3.8%
Other values (71) 30164
28.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 106064
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
17406
16.4%
e 10019
 
9.4%
a 7094
 
6.7%
t 6765
 
6.4%
i 6376
 
6.0%
o 6290
 
5.9%
n 6151
 
5.8%
r 5982
 
5.6%
s 5751
 
5.4%
h 4066
 
3.8%
Other values (71) 30164
28.4%

Meta_score
Real number (ℝ)

Distinct63
Distinct (%)8.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean77.154278
Minimum28
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.7 KiB
2025-08-28T22:08:38.418947image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum28
5-th percentile55
Q170
median78
Q386
95-th percentile96
Maximum100
Range72
Interquartile range (IQR)16

Descriptive statistics

Standard deviation12.409392
Coefficient of variation (CV)0.16083868
Kurtosis0.48825356
Mean77.154278
Median Absolute Deviation (MAD)8
Skewness-0.58460256
Sum55011
Variance153.99302
MonotonicityNot monotonic
2025-08-28T22:08:38.509052image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
76 30
 
4.2%
80 25
 
3.5%
84 25
 
3.5%
72 25
 
3.5%
90 24
 
3.4%
85 23
 
3.2%
74 23
 
3.2%
77 23
 
3.2%
73 23
 
3.2%
82 21
 
2.9%
Other values (53) 471
66.1%
ValueCountFrequency (%)
28 1
 
0.1%
30 1
 
0.1%
33 1
 
0.1%
36 1
 
0.1%
40 1
 
0.1%
41 1
 
0.1%
44 1
 
0.1%
45 3
0.4%
46 1
 
0.1%
47 4
0.6%
ValueCountFrequency (%)
100 11
1.5%
99 3
 
0.4%
98 6
 
0.8%
97 8
1.1%
96 12
1.7%
95 9
1.3%
94 15
2.1%
93 9
1.3%
92 11
1.5%
91 12
1.7%
Distinct402
Distinct (%)56.4%
Missing0
Missing (%)0.0%
Memory size44.5 KiB
2025-08-28T22:08:38.661492image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Length

Max length32
Median length19
Mean length13.398317
Min length7

Characters and Unicode

Total characters9553
Distinct characters63
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique263 ?
Unique (%)36.9%

Sample

1st rowFrancis Ford Coppola
2nd rowChristopher Nolan
3rd rowFrancis Ford Coppola
4th rowSidney Lumet
5th rowPeter Jackson
ValueCountFrequency (%)
david 26
 
1.8%
john 24
 
1.6%
james 20
 
1.4%
martin 15
 
1.0%
robert 15
 
1.0%
richard 14
 
0.9%
steven 14
 
0.9%
spielberg 13
 
0.9%
lee 12
 
0.8%
peter 11
 
0.7%
Other values (633) 1316
88.9%
2025-08-28T22:08:38.884963image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 903
 
9.5%
767
 
8.0%
a 729
 
7.6%
n 719
 
7.5%
r 648
 
6.8%
o 623
 
6.5%
i 585
 
6.1%
l 393
 
4.1%
s 358
 
3.7%
t 327
 
3.4%
Other values (53) 3501
36.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9553
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 903
 
9.5%
767
 
8.0%
a 729
 
7.6%
n 719
 
7.5%
r 648
 
6.8%
o 623
 
6.5%
i 585
 
6.1%
l 393
 
4.1%
s 358
 
3.7%
t 327
 
3.4%
Other values (53) 3501
36.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9553
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 903
 
9.5%
767
 
8.0%
a 729
 
7.6%
n 719
 
7.5%
r 648
 
6.8%
o 623
 
6.5%
i 585
 
6.1%
l 393
 
4.1%
s 358
 
3.7%
t 327
 
3.4%
Other values (53) 3501
36.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9553
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 903
 
9.5%
767
 
8.0%
a 729
 
7.6%
n 719
 
7.5%
r 648
 
6.8%
o 623
 
6.5%
i 585
 
6.1%
l 393
 
4.1%
s 358
 
3.7%
t 327
 
3.4%
Other values (53) 3501
36.6%

Star1
Text

Distinct471
Distinct (%)66.1%
Missing0
Missing (%)0.0%
Memory size44.2 KiB
2025-08-28T22:08:39.045248image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Length

Max length22
Median length19
Mean length12.927069
Min length5

Characters and Unicode

Total characters9217
Distinct characters68
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique355 ?
Unique (%)49.8%

Sample

1st rowMarlon Brando
2nd rowChristian Bale
3rd rowAl Pacino
4th rowHenry Fonda
5th rowElijah Wood
ValueCountFrequency (%)
tom 22
 
1.5%
daniel 17
 
1.2%
robert 13
 
0.9%
john 12
 
0.8%
hanks 12
 
0.8%
ethan 11
 
0.7%
al 11
 
0.7%
pacino 10
 
0.7%
de 10
 
0.7%
clint 10
 
0.7%
Other values (792) 1340
91.3%
2025-08-28T22:08:39.295025image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 821
 
8.9%
e 796
 
8.6%
755
 
8.2%
n 683
 
7.4%
o 581
 
6.3%
i 577
 
6.3%
r 557
 
6.0%
l 466
 
5.1%
t 317
 
3.4%
s 313
 
3.4%
Other values (58) 3351
36.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9217
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 821
 
8.9%
e 796
 
8.6%
755
 
8.2%
n 683
 
7.4%
o 581
 
6.3%
i 577
 
6.3%
r 557
 
6.0%
l 466
 
5.1%
t 317
 
3.4%
s 313
 
3.4%
Other values (58) 3351
36.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9217
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 821
 
8.9%
e 796
 
8.6%
755
 
8.2%
n 683
 
7.4%
o 581
 
6.3%
i 577
 
6.3%
r 557
 
6.0%
l 466
 
5.1%
t 317
 
3.4%
s 313
 
3.4%
Other values (58) 3351
36.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9217
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 821
 
8.9%
e 796
 
8.6%
755
 
8.2%
n 683
 
7.4%
o 581
 
6.3%
i 577
 
6.3%
r 557
 
6.0%
l 466
 
5.1%
t 317
 
3.4%
s 313
 
3.4%
Other values (58) 3351
36.4%

Star2
Text

Distinct598
Distinct (%)83.9%
Missing0
Missing (%)0.0%
Memory size44.5 KiB
2025-08-28T22:08:39.483109image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Length

Max length25
Median length22
Mean length13.239832
Min length5

Characters and Unicode

Total characters9440
Distinct characters67
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique521 ?
Unique (%)73.1%

Sample

1st rowAl Pacino
2nd rowHeath Ledger
3rd rowRobert De Niro
4th rowLee J. Cobb
5th rowViggo Mortensen
ValueCountFrequency (%)
robert 14
 
0.9%
john 13
 
0.9%
michael 10
 
0.7%
lee 10
 
0.7%
emma 10
 
0.7%
chris 9
 
0.6%
tom 8
 
0.5%
james 7
 
0.5%
george 7
 
0.5%
christopher 7
 
0.5%
Other values (1013) 1385
93.6%
2025-08-28T22:08:39.777560image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 918
 
9.7%
a 865
 
9.2%
767
 
8.1%
n 688
 
7.3%
r 641
 
6.8%
i 552
 
5.8%
o 531
 
5.6%
l 445
 
4.7%
t 376
 
4.0%
s 319
 
3.4%
Other values (57) 3338
35.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9440
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 918
 
9.7%
a 865
 
9.2%
767
 
8.1%
n 688
 
7.3%
r 641
 
6.8%
i 552
 
5.8%
o 531
 
5.6%
l 445
 
4.7%
t 376
 
4.0%
s 319
 
3.4%
Other values (57) 3338
35.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9440
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 918
 
9.7%
a 865
 
9.2%
767
 
8.1%
n 688
 
7.3%
r 641
 
6.8%
i 552
 
5.8%
o 531
 
5.6%
l 445
 
4.7%
t 376
 
4.0%
s 319
 
3.4%
Other values (57) 3338
35.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9440
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 918
 
9.7%
a 865
 
9.2%
767
 
8.1%
n 688
 
7.3%
r 641
 
6.8%
i 552
 
5.8%
o 531
 
5.6%
l 445
 
4.7%
t 376
 
4.0%
s 319
 
3.4%
Other values (57) 3338
35.4%

Star3
Text

Distinct625
Distinct (%)87.7%
Missing0
Missing (%)0.0%
Memory size44.4 KiB
2025-08-28T22:08:39.952724image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Length

Max length27
Median length21
Mean length13.28331
Min length5

Characters and Unicode

Total characters9471
Distinct characters69
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique561 ?
Unique (%)78.7%

Sample

1st rowJames Caan
2nd rowAaron Eckhart
3rd rowRobert Duvall
4th rowMartin Balsam
5th rowIan McKellen
ValueCountFrequency (%)
john 17
 
1.2%
robert 14
 
0.9%
michael 11
 
0.7%
richard 8
 
0.5%
jennifer 7
 
0.5%
lee 7
 
0.5%
jack 7
 
0.5%
de 7
 
0.5%
christopher 7
 
0.5%
rupert 7
 
0.5%
Other values (1023) 1382
93.8%
2025-08-28T22:08:40.205206image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 868
 
9.2%
a 846
 
8.9%
761
 
8.0%
n 697
 
7.4%
i 622
 
6.6%
r 610
 
6.4%
o 570
 
6.0%
l 461
 
4.9%
t 327
 
3.5%
s 309
 
3.3%
Other values (59) 3400
35.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9471
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 868
 
9.2%
a 846
 
8.9%
761
 
8.0%
n 697
 
7.4%
i 622
 
6.6%
r 610
 
6.4%
o 570
 
6.0%
l 461
 
4.9%
t 327
 
3.5%
s 309
 
3.3%
Other values (59) 3400
35.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9471
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 868
 
9.2%
a 846
 
8.9%
761
 
8.0%
n 697
 
7.4%
i 622
 
6.6%
r 610
 
6.4%
o 570
 
6.0%
l 461
 
4.9%
t 327
 
3.5%
s 309
 
3.3%
Other values (59) 3400
35.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9471
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 868
 
9.2%
a 846
 
8.9%
761
 
8.0%
n 697
 
7.4%
i 622
 
6.6%
r 610
 
6.4%
o 570
 
6.0%
l 461
 
4.9%
t 327
 
3.5%
s 309
 
3.3%
Other values (59) 3400
35.9%

Star4
Text

Distinct670
Distinct (%)94.0%
Missing0
Missing (%)0.0%
Memory size44.5 KiB
2025-08-28T22:08:40.375444image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Length

Max length27
Median length23
Mean length13.232819
Min length4

Characters and Unicode

Total characters9435
Distinct characters70
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique631 ?
Unique (%)88.5%

Sample

1st rowDiane Keaton
2nd rowMichael Caine
3rd rowDiane Keaton
4th rowJohn Fiedler
5th rowOrlando Bloom
ValueCountFrequency (%)
john 17
 
1.1%
michael 15
 
1.0%
james 10
 
0.7%
mark 8
 
0.5%
richard 8
 
0.5%
lee 7
 
0.5%
bill 7
 
0.5%
charles 6
 
0.4%
jason 6
 
0.4%
paul 6
 
0.4%
Other values (1114) 1393
93.9%
2025-08-28T22:08:40.730240image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 872
 
9.2%
e 837
 
8.9%
770
 
8.2%
n 671
 
7.1%
r 629
 
6.7%
i 622
 
6.6%
o 518
 
5.5%
l 474
 
5.0%
s 324
 
3.4%
t 297
 
3.1%
Other values (60) 3421
36.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9435
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 872
 
9.2%
e 837
 
8.9%
770
 
8.2%
n 671
 
7.1%
r 629
 
6.7%
i 622
 
6.6%
o 518
 
5.5%
l 474
 
5.0%
s 324
 
3.4%
t 297
 
3.1%
Other values (60) 3421
36.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9435
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 872
 
9.2%
e 837
 
8.9%
770
 
8.2%
n 671
 
7.1%
r 629
 
6.7%
i 622
 
6.6%
o 518
 
5.5%
l 474
 
5.0%
s 324
 
3.4%
t 297
 
3.1%
Other values (60) 3421
36.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9435
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 872
 
9.2%
e 837
 
8.9%
770
 
8.2%
n 671
 
7.1%
r 629
 
6.7%
i 622
 
6.6%
o 518
 
5.5%
l 474
 
5.0%
s 324
 
3.4%
t 297
 
3.1%
Other values (60) 3421
36.3%

No_of_Votes
Real number (ℝ)

High correlation  Unique 

Distinct713
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean353348.04
Minimum25229
Maximum2303232
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.7 KiB
2025-08-28T22:08:40.819631image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum25229
5-th percentile38222.6
Q195826
median236311
Q3505918
95-th percentile1034917.4
Maximum2303232
Range2278003
Interquartile range (IQR)410092

Descriptive statistics

Standard deviation346221.17
Coefficient of variation (CV)0.97983044
Kurtosis4.240262
Mean353348.04
Median Absolute Deviation (MAD)167040
Skewness1.8230128
Sum2.5193715 × 108
Variance1.198691 × 1011
MonotonicityNot monotonic
2025-08-28T22:08:40.910380image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1620367 1
 
0.1%
157498 1
 
0.1%
56235 1
 
0.1%
88511 1
 
0.1%
372490 1
 
0.1%
133351 1
 
0.1%
65659 1
 
0.1%
98611 1
 
0.1%
25229 1
 
0.1%
37445 1
 
0.1%
Other values (703) 703
98.6%
ValueCountFrequency (%)
25229 1
0.1%
25938 1
0.1%
26337 1
0.1%
27007 1
0.1%
27067 1
0.1%
27071 1
0.1%
27650 1
0.1%
28003 1
0.1%
28223 1
0.1%
28825 1
0.1%
ValueCountFrequency (%)
2303232 1
0.1%
2067042 1
0.1%
1854740 1
0.1%
1826188 1
0.1%
1809221 1
0.1%
1676426 1
0.1%
1661481 1
0.1%
1642758 1
0.1%
1620367 1
0.1%
1516346 1
0.1%

Gross
Real number (ℝ)

High correlation 

Distinct709
Distinct (%)99.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean78583953
Minimum1305
Maximum9.3666222 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.7 KiB
2025-08-28T22:08:40.999263image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum1305
5-th percentile528032.8
Q16153939
median35000000
Q31.0251579 × 108
95-th percentile3.0354625 × 108
Maximum9.3666222 × 108
Range9.3666092 × 108
Interquartile range (IQR)96361854

Descriptive statistics

Standard deviation1.1504328 × 108
Coefficient of variation (CV)1.4639538
Kurtosis12.202619
Mean78583953
Median Absolute Deviation (MAD)31800000
Skewness2.9227705
Sum5.6030358 × 1010
Variance1.3234956 × 1016
MonotonicityNot monotonic
2025-08-28T22:08:41.087667image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4360000 3
 
0.4%
9600000 2
 
0.3%
25000000 2
 
0.3%
134966411 1
 
0.1%
44785053 1
 
0.1%
435110554 1
 
0.1%
106260000 1
 
0.1%
31800000 1
 
0.1%
4420000 1
 
0.1%
193817 1
 
0.1%
Other values (699) 699
98.0%
ValueCountFrequency (%)
1305 1
0.1%
3600 1
0.1%
8060 1
0.1%
10177 1
0.1%
12562 1
0.1%
14131 1
0.1%
19181 1
0.1%
25812 1
0.1%
45289 1
0.1%
55000 1
0.1%
ValueCountFrequency (%)
936662225 1
0.1%
858373000 1
0.1%
760507625 1
0.1%
678815482 1
0.1%
659325379 1
0.1%
623279547 1
0.1%
608581744 1
0.1%
534858444 1
0.1%
532177324 1
0.1%
448139099 1
0.1%

Interactions

2025-08-28T22:08:35.817598image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-08-28T22:08:33.894713image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-08-28T22:08:34.349473image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-08-28T22:08:34.705605image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-08-28T22:08:35.086834image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-08-28T22:08:35.465227image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-08-28T22:08:35.871466image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-08-28T22:08:33.982381image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-08-28T22:08:34.406125image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-08-28T22:08:34.764956image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-08-28T22:08:35.148274image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-08-28T22:08:35.522313image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-08-28T22:08:35.929144image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-08-28T22:08:34.040692image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-08-28T22:08:34.463592image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-08-28T22:08:34.827409image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-08-28T22:08:35.211267image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-08-28T22:08:35.582304image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-08-28T22:08:35.992299image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-08-28T22:08:34.181736image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-08-28T22:08:34.530202image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-08-28T22:08:34.895153image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-08-28T22:08:35.281309image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-08-28T22:08:35.646914image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-08-28T22:08:36.052351image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-08-28T22:08:34.241732image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-08-28T22:08:34.592527image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-08-28T22:08:34.960716image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-08-28T22:08:35.345494image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-08-28T22:08:35.709593image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-08-28T22:08:36.108566image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-08-28T22:08:34.296226image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-08-28T22:08:34.650231image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-08-28T22:08:35.025380image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-08-28T22:08:35.405389image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-08-28T22:08:35.762927image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Correlations

2025-08-28T22:08:41.143853image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
CertificateGrossIMDB_RatingMeta_scoreNo_of_VotesReleased_YearRuntime
Certificate1.0000.1090.0400.0700.1130.2630.117
Gross0.1091.0000.029-0.0680.6570.1800.216
IMDB_Rating0.0400.0291.0000.2890.399-0.1510.211
Meta_score0.070-0.0680.2891.000-0.012-0.173-0.059
No_of_Votes0.1130.6570.399-0.0121.0000.2330.176
Released_Year0.2630.180-0.151-0.1730.2331.0000.049
Runtime0.1170.2160.211-0.0590.1760.0491.000
2025-08-28T22:08:41.220357image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Released_YearRuntimeIMDB_RatingMeta_scoreNo_of_VotesGross
Released_Year1.000-0.018-0.179-0.2730.2000.235
Runtime-0.0181.0000.258-0.0060.2130.169
IMDB_Rating-0.1790.2581.0000.2840.6090.131
Meta_score-0.273-0.0060.2841.0000.029-0.015
No_of_Votes0.2000.2130.6090.0291.0000.561
Gross0.2350.1690.131-0.0150.5611.000
2025-08-28T22:08:41.294100image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Released_YearRuntimeIMDB_RatingMeta_scoreNo_of_VotesGross
Released_Year1.0000.049-0.151-0.1730.2330.180
Runtime0.0491.0000.211-0.0590.1760.216
IMDB_Rating-0.1510.2111.0000.2890.3990.029
Meta_score-0.173-0.0590.2891.000-0.012-0.068
No_of_Votes0.2330.1760.399-0.0121.0000.657
Gross0.1800.2160.029-0.0680.6571.000
2025-08-28T22:08:41.366098image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Released_YearRuntimeIMDB_RatingMeta_scoreNo_of_VotesGross
Released_Year1.0000.034-0.106-0.1180.1550.125
Runtime0.0341.0000.151-0.0410.1190.148
IMDB_Rating-0.1060.1511.0000.2100.2870.019
Meta_score-0.118-0.0410.2101.000-0.009-0.046
No_of_Votes0.1550.1190.287-0.0091.0000.471
Gross0.1250.1480.019-0.0460.4711.000
2025-08-28T22:08:41.437467image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Released_YearCertificateRuntimeIMDB_RatingMeta_scoreNo_of_VotesGross
Released_Year1.0000.5140.2900.2290.4220.1760.000
Certificate0.5141.0000.2500.0890.1520.2440.235
Runtime0.2900.2501.0000.4650.0810.3430.245
IMDB_Rating0.2290.0890.4651.0000.2600.8240.400
Meta_score0.4220.1520.0810.2601.0000.0000.000
No_of_Votes0.1760.2440.3430.8240.0001.0000.759
Gross0.0000.2350.2450.4000.0000.7591.000

Missing values

2025-08-28T22:08:36.191630image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-08-28T22:08:36.329783image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Series_TitleReleased_YearCertificateRuntimeGenreIMDB_RatingOverviewMeta_scoreDirectorStar1Star2Star3Star4No_of_VotesGross
0The Godfather1972.0A175Crime, Drama9.2An organized crime dynasty's aging patriarch transfers control of his clandestine empire to his reluctant son.100.0Francis Ford CoppolaMarlon BrandoAl PacinoJames CaanDiane Keaton1620367134966411
1The Dark Knight2008.0UA152Action, Crime, Drama9.0When the menace known as the Joker wreaks havoc and chaos on the people of Gotham, Batman must accept one of the greatest psychological and physical tests of his ability to fight injustice.84.0Christopher NolanChristian BaleHeath LedgerAaron EckhartMichael Caine2303232534858444
2The Godfather: Part II1974.0A202Crime, Drama9.0The early life and career of Vito Corleone in 1920s New York City is portrayed, while his son, Michael, expands and tightens his grip on the family crime syndicate.90.0Francis Ford CoppolaAl PacinoRobert De NiroRobert DuvallDiane Keaton112995257300000
312 Angry Men1957.0U96Crime, Drama9.0A jury holdout attempts to prevent a miscarriage of justice by forcing his colleagues to reconsider the evidence.96.0Sidney LumetHenry FondaLee J. CobbMartin BalsamJohn Fiedler6898454360000
4The Lord of the Rings: The Return of the King2003.0U201Action, Adventure, Drama8.9Gandalf and Aragorn lead the World of Men against Sauron's army to draw his gaze from Frodo and Sam as they approach Mount Doom with the One Ring.94.0Peter JacksonElijah WoodViggo MortensenIan McKellenOrlando Bloom1642758377845905
5Pulp Fiction1994.0A154Crime, Drama8.9The lives of two mob hitmen, a boxer, a gangster and his wife, and a pair of diner bandits intertwine in four tales of violence and redemption.94.0Quentin TarantinoJohn TravoltaUma ThurmanSamuel L. JacksonBruce Willis1826188107928762
6Schindler's List1993.0A195Biography, Drama, History8.9In German-occupied Poland during World War II, industrialist Oskar Schindler gradually becomes concerned for his Jewish workforce after witnessing their persecution by the Nazis.94.0Steven SpielbergLiam NeesonRalph FiennesBen KingsleyCaroline Goodall121350596898818
7Inception2010.0UA148Action, Adventure, Sci-Fi8.8A thief who steals corporate secrets through the use of dream-sharing technology is given the inverse task of planting an idea into the mind of a C.E.O.74.0Christopher NolanLeonardo DiCaprioJoseph Gordon-LevittElliot PageKen Watanabe2067042292576195
8Fight Club1999.0A139Drama8.8An insomniac office worker and a devil-may-care soapmaker form an underground fight club that evolves into something much, much more.66.0David FincherBrad PittEdward NortonMeat LoafZach Grenier185474037030102
9The Lord of the Rings: The Fellowship of the Ring2001.0U178Action, Adventure, Drama8.8A meek Hobbit from the Shire and eight companions set out on a journey to destroy the powerful One Ring and save Middle-earth from the Dark Lord Sauron.92.0Peter JacksonElijah WoodIan McKellenOrlando BloomSean Bean1661481315544750
Series_TitleReleased_YearCertificateRuntimeGenreIMDB_RatingOverviewMeta_scoreDirectorStar1Star2Star3Star4No_of_VotesGross
703The Muppet Movie1979.0U95Adventure, Comedy, Family7.6Kermit and his newfound friends trek across America to find success in Hollywood, but a frog legs merchant is after Kermit.74.0James FrawleyJim HensonFrank OzJerry NelsonRichard Hunt3280276657000
704Escape from Alcatraz1979.0A112Action, Biography, Crime7.6Alcatraz is the most secure prison of its time. It is believed that no one can ever escape from it, until three daring men make a possible successful attempt at escaping from one of the most infamous prisons in the world.76.0Don SiegelClint EastwoodPatrick McGoohanRoberts BlossomJack Thibeau12173143000000
705Midnight Express1978.0A121Biography, Crime, Drama7.6Billy Hayes, an American college student, is caught smuggling drugs out of Turkey and thrown into prison.59.0Alan ParkerBrad DavisIrene MiracleBo HopkinsPaolo Bonacelli7366235000000
706Close Encounters of the Third Kind1977.0U138Drama, Sci-Fi7.6Roy Neary, an electric lineman, watches how his quiet and ordinary daily life turns upside down after a close encounter with a UFO.90.0Steven SpielbergRichard DreyfussFrançois TruffautTeri GarrMelinda Dillon184966132088635
707The Long Goodbye1973.0A112Comedy, Crime, Drama7.6Private investigator Philip Marlowe helps a friend out of a jam, but in doing so gets implicated in his wife's murder.87.0Robert AltmanElliott GouldNina van PallandtSterling HaydenMark Rydell26337959000
708Giù la testa1971.0PG157Drama, War, Western7.6A low-life bandit and an I.R.A. explosives expert rebel against the government and become heroes of the Mexican Revolution.77.0Sergio LeoneRod SteigerJames CoburnRomolo ValliMaria Monti30144696690
709Kelly's Heroes1970.0PG144Adventure, Comedy, War7.6A group of U.S. soldiers sneaks across enemy lines to get their hands on a secret stash of Nazi treasure.50.0Brian G. HuttonClint EastwoodTelly SavalasDon RicklesCarroll O'Connor453381378435
710The Jungle Book1967.0U78Animation, Adventure, Family7.6Bagheera the Panther and Baloo the Bear have a difficult time trying to convince a boy to leave the jungle for human civilization.65.0Wolfgang ReithermanPhil HarrisSebastian CabotLouis PrimaBruce Reitherman166409141843612
711A Hard Day's Night1964.0U87Comedy, Music, Musical7.6Over two "typical" days in the life of The Beatles, the boys struggle to keep themselves and Sir Paul McCartney's mischievous grandfather in check while preparing for a live television performance.96.0Richard LesterJohn LennonPaul McCartneyGeorge HarrisonRingo Starr4035113780024
712From Here to Eternity1953.0Approved118Drama, Romance, War7.6In Hawaii in 1941, a private is cruelly punished for not boxing on his unit's team, while his captain's wife and second-in-command are falling in love.85.0Fred ZinnemannBurt LancasterMontgomery CliftDeborah KerrDonna Reed4337430500000